library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)
library(sf)
library(tmap)
library(mapview)

Monthly US renewables

us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>% 
  clean_names()
renew_clean <- us_renew %>%
  mutate(description = str_to_lower(description)) %>% 
  filter(str_detect(description, pattern = "consumption")) %>% # str_detect is logical TRUE/FALSE
  filter(!str_detect(description, pattern = "total"))
renew_date <- renew_clean %>%
  mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>%
  mutate(month_sep = yearmonth(yr_mo_day)) %>% 
  mutate(value = as.numeric(value)) %>% 
  drop_na(month_sep, value)

class(renew_date$month_sep)
## [1] "yearmonth" "Date"
# parse data using lubridate::month()
renew_parsed <- renew_date %>% 
  mutate(month = month(yr_mo_day, label = TRUE)) %>% 
  mutate(year = year(yr_mo_day))

Look at it:

renew_gg <- ggplot(data = renew_date,
                   aes(x = month_sep,
                       y = value,
                       group = description)) +
  geom_line(aes(color = description))

renew_gg

# View(palettes_d_names) # to view all the palletes in paletteer
renew_gg +
  scale_color_paletteer_d("ggsci::default_aaas")

Coerce renew_parsed to a tsibble

renew_ts <-  as_tsibble(renew_parsed,
                       key = description, index = month_sep)
renew_ts %>% autoplot(value)

renew_ts %>%  gg_subseries(value)

renew_ts %>%  gg_season(value)

# to reproduce gg_season using ggplot
# ggplot(data = renew_parsed, aes(x=month, y=value, group = year)) +
#   geom_line(aes(color = year)) +
#   facet_wrap(~description, 
#              ncol = 1,
#              scales = "free",
#              strip.position = "right")

just look at the hydroelectric energy consumption

hydro_ts <- renew_ts %>% 
  filter(description == "hydroelectric power consumption")

hydro_ts %>% autoplot(value) 

hydro_ts %>% gg_subseries(value)

hydro_ts %>% gg_season(value)

quarterly average consumption for hydro

hydro_quarterly <- hydro_ts %>% 
  index_by(year_qu = ~(yearquarter(.))) %>% 
  summarise(avg_consumption = mean(value))

head(hydro_quarterly)
## # A tsibble: 6 x 2 [1Q]
##   year_qu avg_consumption
##     <qtr>           <dbl>
## 1 1973 Q1            261.
## 2 1973 Q2            255.
## 3 1973 Q3            212.
## 4 1973 Q4            225.
## 5 1974 Q1            292.
## 6 1974 Q2            290.

decompose that hydro_ts

dcmp <- hydro_ts %>% 
  model(STL(value ~ season(window = 5)))

components(dcmp) %>% autoplot()

# residual is ussually < 10% of original scale value
hist(components(dcmp)$remainder)

hydro_ts %>% 
  ACF(value) %>% 
  autoplot()

warning, do more research

hydro_model <- hydro_ts %>% 
  model(
    ARIMA(value)
  ) %>% 
  fabletools::forecast(h = "4 years")

hydro_model %>% autoplot(filter(hydro_ts, year(month_sep) > 2010))

make a worldmap in a coupe of minutes

world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
                 layer = "TM_WORLD_BORDERS_SIMPL-0.3")

mapview(world)